A Global Evaluation of Generic Antimicrobial Prescribing Competencies for Use in Veterinary Curricula
Bibliographic record
Abstract
The European Society for Clinical Microbiology and Infectious Diseases (ESCMID) developed consensus-based generic competencies in antimicrobial prescribing and stewardship. These may be useful in structuring and evaluating antimicrobial prescribing education to veterinary students, but their applicability has not been evaluated. We aimed to evaluate whether the ESCMID competencies are currently taught and how relevant they are to veterinary prescribing in veterinary schools globally. A multi-center, cross-sectional survey was performed by administering an online questionnaire to academics teaching antimicrobial prescribing to veterinary students. Targeted recruitment was undertaken to ensure the representation of diverse geographical locations. Responses (48) were received from veterinary schools in Europe (26), North America (7), Asia (6), Australia (3), Central and South America (3), and Africa (3). Of the 37 ESCMID prescribing competencies, only 6 were considered only "slightly" or "not at all" relevant by more than 10% of respondents. Of the 37 competencies, 25 of the competencies were taught in more than 90% of schools and another 6 were taught in 80%-89% of schools. Time spent teaching was "too little" or "far too little" for five competencies according to more than 50% of the respondents. Additional competencies to address extra-label drug use; the use of compounded antimicrobials; the use of antimicrobials for metaphylaxis, prophylaxis, and growth promotion; and the importance rating of antimicrobials were suggested. The ESCMID antimicrobial prescribing competencies had broad relevance and were widely covered in the veterinary curriculum globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".